In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Q: In this paper we have formalized the problem of automatic fill-on-the-blanks quiz generation using two well-defined learning schemes: sequence classification and sequence labeling. 
 Question: Which two schemes are used?

A: sequence classification sequence labeling
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Q: In case of polysemous words, only the first word sense (usually the most common) is taken into account. 
 Question: How do they handle polysemous words in their entity library?

A: only the first word sense (usually the most common) is taken into account
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Q: To implement the first tier of our framework and detect moral relevance, we complement our morally relevant seed words with a corresponding set of seed words approximating moral irrelevance based on the notion of valence, i.e., the degree of pleasantness or unpleasantness of a stimulus. We refer to the emotional valence ratings collected by BIBREF28 for approximately 14,000 English words, and choose the words with most neutral valence rating that do not occur in the MFD as our set of morally irrelevant seed words, for an equal total number of morally relevant and morally irrelevant words. 
 Question: How do they quantify moral relevance?

A:
By complementing morally relevant seed words with a set of morally irrelevant seed words based on the notion of valence
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